PROJECT SUMMARY Alternative polyadenylation (APA) enables the same gene to have multiple 3'UTR ends and affects more than 70% of human genes. By altering polyadenylation sites, APA can create transcripts with different cis-regulatory elements to influence stability and translation. Accumulating evidence has indicated that APA is playing important roles in cancers. For example, CCND1, an oncogene in leukemia, was found to use shorter 3'UTR to escape miRNA repression in proliferating and transformed cells. Our study (Xia, Nature Communications) observed global shortening of 3'UTR in hundreds of tumor samples. Therefore, APA regulators governing widespread 3'UTR shortening in cancer may lead to drug target discoveries for cancer therapy. To this end, our another study (Masamha Nature) identified CFIm25 as a master APA regulator in GBM. However, APA regulators in other cancers still need to be explored. Recently, with the development of bioinformatics tools for APA usage quantification from RNA-seq and wide employment of RNA-seq by large cancer genome consortiums, it is possible to identify APA regulators through computational big data analysis. Our preliminary analyses have identified DNMT3A, a highly mutated gene in acute myeloid leukemia (AML), as a potential APA regulator in AML. Therefore, we hypothesize that a powerful and dedicated computational screening model can be used to reveal APA regulators for cancers through integration APA usage with other molecular features, including gene expression and DNA mutation. The objective of this proposal is to develop such a novel computational method, and apply this method to infer APA regulators from ~15,000 tumor samples across 33 cancer types. These identified master APA regulator genes may sever as novel cancer driver/repressor genes and thus provide new directions for therapeutic target discovery. My career goal is to develop and apply novel computational and systems modeling methods for complex and large-scale clinical data analysis, by doing so, provide novel molecular diagnosis and potential therapeutics for cancer and other diseases. Dr. Adam Margolin, the director of the Computational Biology Program at Oregon Health & Science University (OHSU) and Dr. Brian Druker, the director of OHSU's Knight Cancer Institute, will form a multidisciplinary mentoring team to provided numerous educational opportunities to further enhance my research knowledge in both computational biology and cancer biology. This K01 grant will offer me the protected time to develop essential skills for independent research and the successful future grants application like NIH R01, and thus have a long-term impact on my ability to sustain a career in computational cancer biology field.